Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations1716428
Missing cells3939947
Missing cells (%)13.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory222.6 MiB
Average record size in memory136.0 B

Variable types

Numeric14
Categorical3

Alerts

AMT_CREDIT_SUM_DEBT is highly overall correlated with DAYS_CREDIT_ENDDATE and 1 other fieldsHigh correlation
AMT_CREDIT_SUM_OVERDUE is highly overall correlated with CREDIT_DAY_OVERDUEHigh correlation
CREDIT_DAY_OVERDUE is highly overall correlated with AMT_CREDIT_SUM_OVERDUEHigh correlation
DAYS_CREDIT is highly overall correlated with DAYS_CREDIT_ENDDATE and 2 other fieldsHigh correlation
DAYS_CREDIT_ENDDATE is highly overall correlated with AMT_CREDIT_SUM_DEBT and 3 other fieldsHigh correlation
DAYS_CREDIT_UPDATE is highly overall correlated with AMT_CREDIT_SUM_DEBT and 3 other fieldsHigh correlation
DAYS_ENDDATE_FACT is highly overall correlated with DAYS_CREDIT and 2 other fieldsHigh correlation
CREDIT_ACTIVE is highly imbalanced (50.9%) Imbalance
CREDIT_CURRENCY is highly imbalanced (99.5%) Imbalance
CREDIT_TYPE is highly imbalanced (72.7%) Imbalance
DAYS_CREDIT_ENDDATE has 105553 (6.1%) missing values Missing
DAYS_ENDDATE_FACT has 633653 (36.9%) missing values Missing
AMT_CREDIT_MAX_OVERDUE has 1124488 (65.5%) missing values Missing
AMT_CREDIT_SUM_DEBT has 257669 (15.0%) missing values Missing
AMT_CREDIT_SUM_LIMIT has 591780 (34.5%) missing values Missing
AMT_ANNUITY has 1226791 (71.5%) missing values Missing
CREDIT_DAY_OVERDUE is highly skewed (γ1 = 55.93100542) Skewed
AMT_CREDIT_MAX_OVERDUE is highly skewed (γ1 = 470.9138195) Skewed
CNT_CREDIT_PROLONG is highly skewed (γ1 = 20.31927659) Skewed
AMT_CREDIT_SUM is highly skewed (γ1 = 124.5860969) Skewed
AMT_CREDIT_SUM_DEBT is highly skewed (γ1 = 36.41453834) Skewed
AMT_CREDIT_SUM_OVERDUE is highly skewed (γ1 = 403.2418584) Skewed
AMT_ANNUITY is highly skewed (γ1 = 212.5431248) Skewed
SK_ID_BUREAU has unique values Unique
CREDIT_DAY_OVERDUE has 1712211 (99.8%) zeros Zeros
AMT_CREDIT_MAX_OVERDUE has 470650 (27.4%) zeros Zeros
CNT_CREDIT_PROLONG has 1707314 (99.5%) zeros Zeros
AMT_CREDIT_SUM has 66582 (3.9%) zeros Zeros
AMT_CREDIT_SUM_DEBT has 1016434 (59.2%) zeros Zeros
AMT_CREDIT_SUM_LIMIT has 1050142 (61.2%) zeros Zeros
AMT_CREDIT_SUM_OVERDUE has 1712270 (99.8%) zeros Zeros
AMT_ANNUITY has 256915 (15.0%) zeros Zeros

Reproduction

Analysis started2025-01-18 11:52:17.160309
Analysis finished2025-01-18 11:54:27.884371
Duration2 minutes and 10.72 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

SK_ID_CURR
Real number (ℝ)

Distinct305811
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean278214.93
Minimum100001
Maximum456255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.1 MiB
2025-01-18T12:54:28.349315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum100001
5-th percentile117919
Q1188866.75
median278055
Q3367426
95-th percentile438632
Maximum456255
Range356254
Interquartile range (IQR)178559.25

Descriptive statistics

Standard deviation102938.56
Coefficient of variation (CV)0.36999652
Kurtosis-1.2027765
Mean278214.93
Median Absolute Deviation (MAD)89275
Skewness0.0010628877
Sum4.775359 × 1011
Variance1.0596347 × 1010
MonotonicityNot monotonic
2025-01-18T12:54:28.529522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120860 116
 
< 0.1%
169704 94
 
< 0.1%
318065 78
 
< 0.1%
251643 61
 
< 0.1%
425396 60
 
< 0.1%
295809 59
 
< 0.1%
129843 58
 
< 0.1%
385133 57
 
< 0.1%
177014 56
 
< 0.1%
280155 55
 
< 0.1%
Other values (305801) 1715734
> 99.9%
ValueCountFrequency (%)
100001 7
 
< 0.1%
100002 8
< 0.1%
100003 4
 
< 0.1%
100004 2
 
< 0.1%
100005 3
 
< 0.1%
100007 1
 
< 0.1%
100008 3
 
< 0.1%
100009 18
< 0.1%
100010 2
 
< 0.1%
100011 4
 
< 0.1%
ValueCountFrequency (%)
456255 11
< 0.1%
456254 1
 
< 0.1%
456253 4
 
< 0.1%
456250 3
 
< 0.1%
456249 13
< 0.1%
456247 11
< 0.1%
456246 3
 
< 0.1%
456244 23
< 0.1%
456243 7
 
< 0.1%
456242 1
 
< 0.1%

SK_ID_BUREAU
Real number (ℝ)

Unique 

Distinct1716428
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5924434.5
Minimum5000000
Maximum6843457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.1 MiB
2025-01-18T12:54:28.709513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5000000
5-th percentile5092405.3
Q15463953.8
median5926303.5
Q36385681.2
95-th percentile6751974.7
Maximum6843457
Range1843457
Interquartile range (IQR)921727.5

Descriptive statistics

Standard deviation532265.73
Coefficient of variation (CV)0.089842453
Kurtosis-1.1990158
Mean5924434.5
Median Absolute Deviation (MAD)460849.5
Skewness-0.0074978321
Sum1.0168865 × 1013
Variance2.8330681 × 1011
MonotonicityNot monotonic
2025-01-18T12:54:28.869475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5714462 1
 
< 0.1%
6758530 1
 
< 0.1%
6758496 1
 
< 0.1%
6758495 1
 
< 0.1%
6758494 1
 
< 0.1%
6758493 1
 
< 0.1%
6758492 1
 
< 0.1%
6758491 1
 
< 0.1%
6758490 1
 
< 0.1%
6758489 1
 
< 0.1%
Other values (1716418) 1716418
> 99.9%
ValueCountFrequency (%)
5000000 1
< 0.1%
5000001 1
< 0.1%
5000002 1
< 0.1%
5000003 1
< 0.1%
5000004 1
< 0.1%
5000005 1
< 0.1%
5000006 1
< 0.1%
5000009 1
< 0.1%
5000010 1
< 0.1%
5000011 1
< 0.1%
ValueCountFrequency (%)
6843457 1
< 0.1%
6843456 1
< 0.1%
6843455 1
< 0.1%
6843454 1
< 0.1%
6843453 1
< 0.1%
6843452 1
< 0.1%
6843451 1
< 0.1%
6843450 1
< 0.1%
6843447 1
< 0.1%
6843446 1
< 0.1%

CREDIT_ACTIVE
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.1 MiB
Closed
1079273 
Active
630607 
Sold
 
6527
Bad debt
 
21

Length

Max length8
Median length6
Mean length5.9924191
Min length4

Characters and Unicode

Total characters10285556
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClosed
2nd rowActive
3rd rowActive
4th rowActive
5th rowActive

Common Values

ValueCountFrequency (%)
Closed 1079273
62.9%
Active 630607
36.7%
Sold 6527
 
0.4%
Bad debt 21
 
< 0.1%

Length

2025-01-18T12:54:29.022091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-18T12:54:29.189444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
closed 1079273
62.9%
active 630607
36.7%
sold 6527
 
0.4%
bad 21
 
< 0.1%
debt 21
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 1709901
16.6%
d 1085842
10.6%
l 1085800
10.6%
o 1085800
10.6%
C 1079273
10.5%
s 1079273
10.5%
t 630628
 
6.1%
A 630607
 
6.1%
c 630607
 
6.1%
i 630607
 
6.1%
Other values (6) 637218
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8569107
83.3%
Uppercase Letter 1716428
 
16.7%
Space Separator 21
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1709901
20.0%
d 1085842
12.7%
l 1085800
12.7%
o 1085800
12.7%
s 1079273
12.6%
t 630628
 
7.4%
c 630607
 
7.4%
i 630607
 
7.4%
v 630607
 
7.4%
a 21
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
C 1079273
62.9%
A 630607
36.7%
S 6527
 
0.4%
B 21
 
< 0.1%
Space Separator
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10285535
> 99.9%
Common 21
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1709901
16.6%
d 1085842
10.6%
l 1085800
10.6%
o 1085800
10.6%
C 1079273
10.5%
s 1079273
10.5%
t 630628
 
6.1%
A 630607
 
6.1%
c 630607
 
6.1%
i 630607
 
6.1%
Other values (5) 637197
 
6.2%
Common
ValueCountFrequency (%)
21
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10285556
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1709901
16.6%
d 1085842
10.6%
l 1085800
10.6%
o 1085800
10.6%
C 1079273
10.5%
s 1079273
10.5%
t 630628
 
6.1%
A 630607
 
6.1%
c 630607
 
6.1%
i 630607
 
6.1%
Other values (6) 637218
 
6.2%

CREDIT_CURRENCY
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.1 MiB
currency 1
1715020 
currency 2
 
1224
currency 3
 
174
currency 4
 
10

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters17164280
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcurrency 1
2nd rowcurrency 1
3rd rowcurrency 1
4th rowcurrency 1
5th rowcurrency 1

Common Values

ValueCountFrequency (%)
currency 1 1715020
99.9%
currency 2 1224
 
0.1%
currency 3 174
 
< 0.1%
currency 4 10
 
< 0.1%

Length

2025-01-18T12:54:29.329650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-18T12:54:29.454207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
currency 1716428
50.0%
1 1715020
50.0%
2 1224
 
< 0.1%
3 174
 
< 0.1%
4 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
c 3432856
20.0%
r 3432856
20.0%
u 1716428
10.0%
e 1716428
10.0%
n 1716428
10.0%
y 1716428
10.0%
1716428
10.0%
1 1715020
10.0%
2 1224
 
< 0.1%
3 174
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13731424
80.0%
Space Separator 1716428
 
10.0%
Decimal Number 1716428
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 3432856
25.0%
r 3432856
25.0%
u 1716428
12.5%
e 1716428
12.5%
n 1716428
12.5%
y 1716428
12.5%
Decimal Number
ValueCountFrequency (%)
1 1715020
99.9%
2 1224
 
0.1%
3 174
 
< 0.1%
4 10
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1716428
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13731424
80.0%
Common 3432856
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 3432856
25.0%
r 3432856
25.0%
u 1716428
12.5%
e 1716428
12.5%
n 1716428
12.5%
y 1716428
12.5%
Common
ValueCountFrequency (%)
1716428
50.0%
1 1715020
50.0%
2 1224
 
< 0.1%
3 174
 
< 0.1%
4 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17164280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 3432856
20.0%
r 3432856
20.0%
u 1716428
10.0%
e 1716428
10.0%
n 1716428
10.0%
y 1716428
10.0%
1716428
10.0%
1 1715020
10.0%
2 1224
 
< 0.1%
3 174
 
< 0.1%

DAYS_CREDIT
Real number (ℝ)

High correlation 

Distinct2923
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1142.1077
Minimum-2922
Maximum0
Zeros25
Zeros (%)< 0.1%
Negative1716403
Negative (%)> 99.9%
Memory size13.1 MiB
2025-01-18T12:54:29.599429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-2922
5-th percentile-2665
Q1-1666
median-987
Q3-474
95-th percentile-125
Maximum0
Range2922
Interquartile range (IQR)1192

Descriptive statistics

Standard deviation795.16493
Coefficient of variation (CV)-0.69622588
Kurtosis-0.73544529
Mean-1142.1077
Median Absolute Deviation (MAD)570
Skewness-0.58234905
Sum-1.9603456 × 109
Variance632287.26
MonotonicityNot monotonic
2025-01-18T12:54:30.039327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-364 1330
 
0.1%
-336 1248
 
0.1%
-273 1238
 
0.1%
-357 1218
 
0.1%
-343 1203
 
0.1%
-315 1202
 
0.1%
-371 1196
 
0.1%
-365 1194
 
0.1%
-210 1193
 
0.1%
-245 1192
 
0.1%
Other values (2913) 1704214
99.3%
ValueCountFrequency (%)
-2922 278
< 0.1%
-2921 283
< 0.1%
-2920 317
< 0.1%
-2919 344
< 0.1%
-2918 329
< 0.1%
-2917 292
< 0.1%
-2916 296
< 0.1%
-2915 299
< 0.1%
-2914 317
< 0.1%
-2913 317
< 0.1%
ValueCountFrequency (%)
0 25
 
< 0.1%
-1 17
 
< 0.1%
-2 42
 
< 0.1%
-3 74
 
< 0.1%
-4 113
 
< 0.1%
-5 146
< 0.1%
-6 184
< 0.1%
-7 251
< 0.1%
-8 319
< 0.1%
-9 298
< 0.1%

CREDIT_DAY_OVERDUE
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct942
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81816656
Minimum0
Maximum2792
Zeros1712211
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size13.1 MiB
2025-01-18T12:54:30.179691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2792
Range2792
Interquartile range (IQR)0

Descriptive statistics

Standard deviation36.544428
Coefficient of variation (CV)44.666245
Kurtosis3374.4841
Mean0.81816656
Median Absolute Deviation (MAD)0
Skewness55.931005
Sum1404324
Variance1335.4952
MonotonicityNot monotonic
2025-01-18T12:54:30.369418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1712211
99.8%
30 311
 
< 0.1%
60 126
 
< 0.1%
8 103
 
< 0.1%
13 103
 
< 0.1%
9 93
 
< 0.1%
7 92
 
< 0.1%
14 91
 
< 0.1%
17 77
 
< 0.1%
11 75
 
< 0.1%
Other values (932) 3146
 
0.2%
ValueCountFrequency (%)
0 1712211
99.8%
1 5
 
< 0.1%
2 18
 
< 0.1%
3 29
 
< 0.1%
4 46
 
< 0.1%
5 51
 
< 0.1%
6 59
 
< 0.1%
7 92
 
< 0.1%
8 103
 
< 0.1%
9 93
 
< 0.1%
ValueCountFrequency (%)
2792 1
< 0.1%
2781 1
< 0.1%
2776 1
< 0.1%
2770 1
< 0.1%
2766 1
< 0.1%
2765 1
< 0.1%
2754 1
< 0.1%
2703 1
< 0.1%
2700 1
< 0.1%
2693 1
< 0.1%

DAYS_CREDIT_ENDDATE
Real number (ℝ)

High correlation  Missing 

Distinct14096
Distinct (%)0.9%
Missing105553
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean510.51736
Minimum-42060
Maximum31199
Zeros883
Zeros (%)0.1%
Negative1007389
Negative (%)58.7%
Memory size13.1 MiB
2025-01-18T12:54:30.534171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-42060
5-th percentile-2262
Q1-1138
median-330
Q3474
95-th percentile2623
Maximum31199
Range73259
Interquartile range (IQR)1612

Descriptive statistics

Standard deviation4994.2198
Coefficient of variation (CV)9.782664
Kurtosis28.180287
Mean510.51736
Median Absolute Deviation (MAD)806
Skewness5.1271335
Sum8.2237966 × 108
Variance24942232
MonotonicityNot monotonic
2025-01-18T12:54:30.709485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 883
 
0.1%
3 845
 
< 0.1%
-7 837
 
< 0.1%
1 830
 
< 0.1%
-14 787
 
< 0.1%
-10 782
 
< 0.1%
4 777
 
< 0.1%
-2 772
 
< 0.1%
-1 771
 
< 0.1%
-42 768
 
< 0.1%
Other values (14086) 1602823
93.4%
(Missing) 105553
 
6.1%
ValueCountFrequency (%)
-42060 1
 
< 0.1%
-42056 1
 
< 0.1%
-42042 3
< 0.1%
-42041 1
 
< 0.1%
-42013 1
 
< 0.1%
-41938 1
 
< 0.1%
-41920 1
 
< 0.1%
-41910 1
 
< 0.1%
-41905 1
 
< 0.1%
-41899 1
 
< 0.1%
ValueCountFrequency (%)
31199 1
 
< 0.1%
31198 89
< 0.1%
31197 63
< 0.1%
31196 50
 
< 0.1%
31195 103
< 0.1%
31194 122
< 0.1%
31193 150
< 0.1%
31192 110
< 0.1%
31191 108
< 0.1%
31190 90
< 0.1%

DAYS_ENDDATE_FACT
Real number (ℝ)

High correlation  Missing 

Distinct2917
Distinct (%)0.3%
Missing633653
Missing (%)36.9%
Infinite0
Infinite (%)0.0%
Mean-1017.4371
Minimum-42023
Maximum0
Zeros64
Zeros (%)< 0.1%
Negative1082711
Negative (%)63.1%
Memory size13.1 MiB
2025-01-18T12:54:30.879168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-42023
5-th percentile-2393
Q1-1489
median-897
Q3-425
95-th percentile-94
Maximum0
Range42023
Interquartile range (IQR)1064

Descriptive statistics

Standard deviation714.01063
Coefficient of variation (CV)-0.70177369
Kurtosis9.4091943
Mean-1017.4371
Median Absolute Deviation (MAD)513
Skewness-0.77475383
Sum-1.1016555 × 109
Variance509811.17
MonotonicityNot monotonic
2025-01-18T12:54:31.039858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-329 811
 
< 0.1%
-273 794
 
< 0.1%
-301 791
 
< 0.1%
-91 785
 
< 0.1%
-154 783
 
< 0.1%
-84 783
 
< 0.1%
-182 782
 
< 0.1%
-238 778
 
< 0.1%
-210 778
 
< 0.1%
-350 773
 
< 0.1%
Other values (2907) 1074917
62.6%
(Missing) 633653
36.9%
ValueCountFrequency (%)
-42023 1
< 0.1%
-3042 1
< 0.1%
-2922 1
< 0.1%
-2919 1
< 0.1%
-2917 1
< 0.1%
-2916 2
< 0.1%
-2915 2
< 0.1%
-2914 2
< 0.1%
-2913 1
< 0.1%
-2912 2
< 0.1%
ValueCountFrequency (%)
0 64
 
< 0.1%
-1 217
< 0.1%
-2 162
 
< 0.1%
-3 223
< 0.1%
-4 265
< 0.1%
-5 373
< 0.1%
-6 369
< 0.1%
-7 429
< 0.1%
-8 411
< 0.1%
-9 414
< 0.1%

AMT_CREDIT_MAX_OVERDUE
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct68251
Distinct (%)11.5%
Missing1124488
Missing (%)65.5%
Infinite0
Infinite (%)0.0%
Mean3825.4177
Minimum0
Maximum1.1598718 × 108
Zeros470650
Zeros (%)27.4%
Negative0
Negative (%)0.0%
Memory size13.1 MiB
2025-01-18T12:54:31.199532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14220.452
Maximum1.1598718 × 108
Range1.1598718 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation206031.61
Coefficient of variation (CV)53.858591
Kurtosis245696.92
Mean3825.4177
Median Absolute Deviation (MAD)0
Skewness470.91382
Sum2.2644177 × 109
Variance4.2449023 × 1010
MonotonicityNot monotonic
2025-01-18T12:54:31.409273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 470650
27.4%
1440 688
 
< 0.1%
225 405
 
< 0.1%
45 377
 
< 0.1%
4.5 315
 
< 0.1%
90 222
 
< 0.1%
4500 220
 
< 0.1%
2700 192
 
< 0.1%
9000 192
 
< 0.1%
5400 189
 
< 0.1%
Other values (68241) 118490
 
6.9%
(Missing) 1124488
65.5%
ValueCountFrequency (%)
0 470650
27.4%
0.045 17
 
< 0.1%
0.09 4
 
< 0.1%
0.135 12
 
< 0.1%
0.18 5
 
< 0.1%
0.225 6
 
< 0.1%
0.27 2
 
< 0.1%
0.315 8
 
< 0.1%
0.36 4
 
< 0.1%
0.405 6
 
< 0.1%
ValueCountFrequency (%)
115987185 1
< 0.1%
94812246 1
< 0.1%
16950010.5 1
< 0.1%
14111390.7 1
< 0.1%
13975258.5 1
< 0.1%
13766418 1
< 0.1%
13144527 1
< 0.1%
11478060 1
< 0.1%
11246044.5 1
< 0.1%
10861812 1
< 0.1%

CNT_CREDIT_PROLONG
Real number (ℝ)

Skewed  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0064104058
Minimum0
Maximum9
Zeros1707314
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size13.1 MiB
2025-01-18T12:54:31.554168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.096223906
Coefficient of variation (CV)15.010579
Kurtosis615.43877
Mean0.0064104058
Median Absolute Deviation (MAD)0
Skewness20.319277
Sum11003
Variance0.00925904
MonotonicityNot monotonic
2025-01-18T12:54:31.673212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 1707314
99.5%
1 7620
 
0.4%
2 1222
 
0.1%
3 191
 
< 0.1%
4 54
 
< 0.1%
5 21
 
< 0.1%
9 2
 
< 0.1%
6 2
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 1707314
99.5%
1 7620
 
0.4%
2 1222
 
0.1%
3 191
 
< 0.1%
4 54
 
< 0.1%
5 21
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
9 2
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 2
 
< 0.1%
5 21
 
< 0.1%
4 54
 
< 0.1%
3 191
 
< 0.1%
2 1222
 
0.1%
1 7620
 
0.4%
0 1707314
99.5%

AMT_CREDIT_SUM
Real number (ℝ)

Skewed  Zeros 

Distinct236708
Distinct (%)13.8%
Missing13
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean354994.59
Minimum0
Maximum5.85 × 108
Zeros66582
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size13.1 MiB
2025-01-18T12:54:31.809564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11250
Q151300
median125518.5
Q3315000
95-th percentile1350000
Maximum5.85 × 108
Range5.85 × 108
Interquartile range (IQR)263700

Descriptive statistics

Standard deviation1149811.3
Coefficient of variation (CV)3.2389545
Kurtosis49315.967
Mean354994.59
Median Absolute Deviation (MAD)93451.5
Skewness124.5861
Sum6.0931804 × 1011
Variance1.3220661 × 1012
MonotonicityNot monotonic
2025-01-18T12:54:31.979118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 66582
 
3.9%
225000 57608
 
3.4%
135000 50195
 
2.9%
450000 37156
 
2.2%
90000 36940
 
2.2%
180000 28840
 
1.7%
45000 26570
 
1.5%
67500 25444
 
1.5%
270000 22467
 
1.3%
675000 20581
 
1.2%
Other values (236698) 1344032
78.3%
ValueCountFrequency (%)
0 66582
3.9%
0.45 80
 
< 0.1%
2.565 1
 
< 0.1%
4.5 546
 
< 0.1%
9 10
 
< 0.1%
13.5 13
 
< 0.1%
14.13 1
 
< 0.1%
18 3
 
< 0.1%
21.645 1
 
< 0.1%
22.5 2
 
< 0.1%
ValueCountFrequency (%)
585000000 1
 
< 0.1%
396000000 1
 
< 0.1%
170100000 1
 
< 0.1%
164032200 1
 
< 0.1%
146958507 1
 
< 0.1%
142290000 1
 
< 0.1%
135000000 3
< 0.1%
132750000 6
< 0.1%
112500000 1
 
< 0.1%
106745400 1
 
< 0.1%

AMT_CREDIT_SUM_DEBT
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct226537
Distinct (%)15.5%
Missing257669
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean137085.12
Minimum-4705600.3
Maximum1.701 × 108
Zeros1016434
Zeros (%)59.2%
Negative8418
Negative (%)0.5%
Memory size13.1 MiB
2025-01-18T12:54:32.120968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-4705600.3
5-th percentile0
Q10
median0
Q340153.5
95-th percentile628902.45
Maximum1.701 × 108
Range1.748056 × 108
Interquartile range (IQR)40153.5

Descriptive statistics

Standard deviation677401.13
Coefficient of variation (CV)4.9414636
Kurtosis5673.4343
Mean137085.12
Median Absolute Deviation (MAD)0
Skewness36.414538
Sum1.9997415 × 1011
Variance4.5887229 × 1011
MonotonicityNot monotonic
2025-01-18T12:54:32.274857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1016434
59.2%
4.5 653
 
< 0.1%
-450 543
 
< 0.1%
135000 344
 
< 0.1%
90000 320
 
< 0.1%
45000 316
 
< 0.1%
22500 307
 
< 0.1%
67500 238
 
< 0.1%
225000 237
 
< 0.1%
13500 205
 
< 0.1%
Other values (226527) 439162
25.6%
(Missing) 257669
 
15.0%
ValueCountFrequency (%)
-4705600.32 1
< 0.1%
-3109510.98 1
< 0.1%
-2796723.72 1
< 0.1%
-2273021.73 1
< 0.1%
-2167229.34 1
< 0.1%
-2089184.31 1
< 0.1%
-2014753.455 1
< 0.1%
-1764858.06 1
< 0.1%
-1354875.615 1
< 0.1%
-1093553.235 1
< 0.1%
ValueCountFrequency (%)
170100000 1
< 0.1%
164032200 1
< 0.1%
65441403 1
< 0.1%
64570243.5 1
< 0.1%
62218953 1
< 0.1%
59637690 1
< 0.1%
51750000 1
< 0.1%
51365155.5 1
< 0.1%
47406861 1
< 0.1%
44968383 1
< 0.1%

AMT_CREDIT_SUM_LIMIT
Real number (ℝ)

Missing  Zeros 

Distinct51726
Distinct (%)4.6%
Missing591780
Missing (%)34.5%
Infinite0
Infinite (%)0.0%
Mean6229.515
Minimum-586406.11
Maximum4705600.3
Zeros1050142
Zeros (%)61.2%
Negative351
Negative (%)< 0.1%
Memory size13.1 MiB
2025-01-18T12:54:32.420664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-586406.11
5-th percentile0
Q10
median0
Q30
95-th percentile5736.0667
Maximum4705600.3
Range5292006.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation45032.031
Coefficient of variation (CV)7.2288182
Kurtosis796.09609
Mean6229.515
Median Absolute Deviation (MAD)0
Skewness18.026914
Sum7.0060116 × 109
Variance2.0278839 × 109
MonotonicityNot monotonic
2025-01-18T12:54:32.559308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1050142
61.2%
135000 2178
 
0.1%
4500 1474
 
0.1%
45000 1335
 
0.1%
90000 974
 
0.1%
13500 833
 
< 0.1%
22500 766
 
< 0.1%
225000 757
 
< 0.1%
67500 678
 
< 0.1%
450000 558
 
< 0.1%
Other values (51716) 64953
 
3.8%
(Missing) 591780
34.5%
ValueCountFrequency (%)
-586406.115 1
< 0.1%
-401346.945 1
< 0.1%
-399166.875 1
< 0.1%
-372598.245 1
< 0.1%
-316391.895 1
< 0.1%
-255704.355 1
< 0.1%
-250138.845 1
< 0.1%
-234151.065 1
< 0.1%
-223123.05 1
< 0.1%
-216620.01 1
< 0.1%
ValueCountFrequency (%)
4705600.32 1
< 0.1%
4500000 2
< 0.1%
4443255 1
< 0.1%
3555065.655 1
< 0.1%
3375000 2
< 0.1%
3109510.98 1
< 0.1%
3037500 1
< 0.1%
2801223.72 1
< 0.1%
2700000 2
< 0.1%
2648700 1
< 0.1%

AMT_CREDIT_SUM_OVERDUE
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct1616
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.912758
Minimum0
Maximum3756681
Zeros1712270
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size13.1 MiB
2025-01-18T12:54:32.707982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3756681
Range3756681
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5937.65
Coefficient of variation (CV)156.61351
Kurtosis211836.85
Mean37.912758
Median Absolute Deviation (MAD)0
Skewness403.24186
Sum65074519
Variance35255688
MonotonicityNot monotonic
2025-01-18T12:54:32.864304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1712270
99.8%
4.5 301
 
< 0.1%
9 107
 
< 0.1%
13.5 81
 
< 0.1%
18 72
 
< 0.1%
22.5 60
 
< 0.1%
45 56
 
< 0.1%
27 52
 
< 0.1%
36 50
 
< 0.1%
31.5 48
 
< 0.1%
Other values (1606) 3331
 
0.2%
ValueCountFrequency (%)
0 1712270
99.8%
0.045 3
 
< 0.1%
0.27 3
 
< 0.1%
0.315 1
 
< 0.1%
0.36 3
 
< 0.1%
0.675 1
 
< 0.1%
0.72 1
 
< 0.1%
0.765 1
 
< 0.1%
0.81 1
 
< 0.1%
0.855 1
 
< 0.1%
ValueCountFrequency (%)
3756681 1
< 0.1%
3681063 1
< 0.1%
2387232 1
< 0.1%
1851210 1
< 0.1%
1617403.5 1
< 0.1%
1361214 1
< 0.1%
1329597 1
< 0.1%
1224474.885 1
< 0.1%
1125733.5 1
< 0.1%
1097437.5 1
< 0.1%

CREDIT_TYPE
Categorical

Imbalance 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.1 MiB
Consumer credit
1251615 
Credit card
402195 
Car loan
 
27690
Mortgage
 
18391
Microloan
 
12413
Other values (10)
 
4124

Length

Max length44
Median length15
Mean length13.858992
Min length8

Characters and Unicode

Total characters23787962
Distinct characters34
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowConsumer credit
2nd rowCredit card
3rd rowConsumer credit
4th rowCredit card
5th rowConsumer credit

Common Values

ValueCountFrequency (%)
Consumer credit 1251615
72.9%
Credit card 402195
 
23.4%
Car loan 27690
 
1.6%
Mortgage 18391
 
1.1%
Microloan 12413
 
0.7%
Loan for business development 1975
 
0.1%
Another type of loan 1017
 
0.1%
Unknown type of loan 555
 
< 0.1%
Loan for working capital replenishment 469
 
< 0.1%
Cash loan (non-earmarked) 56
 
< 0.1%
Other values (5) 52
 
< 0.1%

Length

2025-01-18T12:54:33.044539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
credit 1653811
48.5%
consumer 1251615
36.7%
card 402195
 
11.8%
loan 31813
 
0.9%
car 27690
 
0.8%
mortgage 18391
 
0.5%
microloan 12413
 
0.4%
for 2467
 
0.1%
business 1975
 
0.1%
development 1975
 
0.1%
Other values (20) 6388
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r 3370683
14.2%
e 2935997
12.3%
d 2058041
8.7%
1694305
 
7.1%
C 1681556
 
7.1%
t 1677798
 
7.1%
i 1669634
 
7.0%
c 1666716
 
7.0%
o 1334782
 
5.6%
n 1304025
 
5.5%
Other values (24) 4394425
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20377053
85.7%
Uppercase Letter 1716428
 
7.2%
Space Separator 1694305
 
7.1%
Open Punctuation 60
 
< 0.1%
Close Punctuation 60
 
< 0.1%
Dash Punctuation 56
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 3370683
16.5%
e 2935997
14.4%
d 2058041
10.1%
t 1677798
8.2%
i 1669634
8.2%
c 1666716
8.2%
o 1334782
 
6.6%
n 1304025
 
6.4%
s 1258123
 
6.2%
m 1254138
 
6.2%
Other values (13) 1847116
9.1%
Uppercase Letter
ValueCountFrequency (%)
C 1681556
98.0%
M 30805
 
1.8%
L 2467
 
0.1%
A 1017
 
0.1%
U 555
 
< 0.1%
R 27
 
< 0.1%
I 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1694305
100.0%
Open Punctuation
ValueCountFrequency (%)
( 60
100.0%
Close Punctuation
ValueCountFrequency (%)
) 60
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22093481
92.9%
Common 1694481
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 3370683
15.3%
e 2935997
13.3%
d 2058041
9.3%
C 1681556
7.6%
t 1677798
7.6%
i 1669634
7.6%
c 1666716
7.5%
o 1334782
 
6.0%
n 1304025
 
5.9%
s 1258123
 
5.7%
Other values (20) 3136126
14.2%
Common
ValueCountFrequency (%)
1694305
> 99.9%
( 60
 
< 0.1%
) 60
 
< 0.1%
- 56
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23787962
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 3370683
14.2%
e 2935997
12.3%
d 2058041
8.7%
1694305
 
7.1%
C 1681556
 
7.1%
t 1677798
 
7.1%
i 1669634
 
7.0%
c 1666716
 
7.0%
o 1334782
 
5.6%
n 1304025
 
5.5%
Other values (24) 4394425
18.5%

DAYS_CREDIT_UPDATE
Real number (ℝ)

High correlation 

Distinct2982
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-593.74832
Minimum-41947
Maximum372
Zeros605
Zeros (%)< 0.1%
Negative1715806
Negative (%)> 99.9%
Memory size13.1 MiB
2025-01-18T12:54:33.271491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-41947
5-th percentile-2079
Q1-908
median-395
Q3-33
95-th percentile-8
Maximum372
Range42319
Interquartile range (IQR)875

Descriptive statistics

Standard deviation720.74731
Coefficient of variation (CV)-1.2138936
Kurtosis596.37366
Mean-593.74832
Median Absolute Deviation (MAD)372
Skewness-11.334995
Sum-1.0191262 × 109
Variance519476.69
MonotonicityNot monotonic
2025-01-18T12:54:33.459424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7 18503
 
1.1%
-8 18462
 
1.1%
-11 16975
 
1.0%
-15 16870
 
1.0%
-12 16827
 
1.0%
-10 16651
 
1.0%
-9 16546
 
1.0%
-13 16387
 
1.0%
-6 16281
 
0.9%
-14 16210
 
0.9%
Other values (2972) 1546716
90.1%
ValueCountFrequency (%)
-41947 1
< 0.1%
-41946 2
< 0.1%
-41945 1
< 0.1%
-41943 2
< 0.1%
-41940 1
< 0.1%
-41936 2
< 0.1%
-41934 2
< 0.1%
-41933 1
< 0.1%
-41931 1
< 0.1%
-41926 1
< 0.1%
ValueCountFrequency (%)
372 1
 
< 0.1%
23 2
< 0.1%
22 1
 
< 0.1%
20 2
< 0.1%
19 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
13 4
< 0.1%
12 1
 
< 0.1%

AMT_ANNUITY
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct40321
Distinct (%)8.2%
Missing1226791
Missing (%)71.5%
Infinite0
Infinite (%)0.0%
Mean15712.758
Minimum0
Maximum1.1845342 × 108
Zeros256915
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size13.1 MiB
2025-01-18T12:54:33.629446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313500
95-th percentile46571.4
Maximum1.1845342 × 108
Range1.1845342 × 108
Interquartile range (IQR)13500

Descriptive statistics

Standard deviation325826.95
Coefficient of variation (CV)20.736459
Kurtosis58560.694
Mean15712.758
Median Absolute Deviation (MAD)0
Skewness212.54312
Sum7.6935475 × 109
Variance1.061632 × 1011
MonotonicityNot monotonic
2025-01-18T12:54:33.819227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 256915
 
15.0%
4500 5182
 
0.3%
13500 3147
 
0.2%
22500 2502
 
0.1%
9000 1725
 
0.1%
18000 1605
 
0.1%
45000 1593
 
0.1%
27000 1252
 
0.1%
2700 1208
 
0.1%
6750 1164
 
0.1%
Other values (40311) 213344
 
12.4%
(Missing) 1226791
71.5%
ValueCountFrequency (%)
0 256915
15.0%
0.045 62
 
< 0.1%
0.315 1
 
< 0.1%
0.45 75
 
< 0.1%
1.395 1
 
< 0.1%
1.44 2
 
< 0.1%
1.8 1
 
< 0.1%
2.16 1
 
< 0.1%
3.375 1
 
< 0.1%
3.6 1
 
< 0.1%
ValueCountFrequency (%)
118453423.5 1
< 0.1%
90632371.5 1
< 0.1%
59586682.5 1
< 0.1%
57476227.5 1
< 0.1%
56844981 1
< 0.1%
54562657.5 1
< 0.1%
45490630.5 1
< 0.1%
43286215.5 1
< 0.1%
42812998.2 1
< 0.1%
33784668 1
< 0.1%

Interactions

2025-01-18T12:54:15.177078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:05.329489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:10.172239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:15.553485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:21.884914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:26.993259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:32.139925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:36.697451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:39.858061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:45.307222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:51.725863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:58.609258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:03.259230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:09.120162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:15.388798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:05.759638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:10.529237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:16.052734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:22.352744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:27.399455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:32.515612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:36.929236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:40.269171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:45.840235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:52.214559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:59.088260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:03.729654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:09.590424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:15.609998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:06.205323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:11.079805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:16.557691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:22.784387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:27.773991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:32.867786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:37.173741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:40.659432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:46.340460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:52.677380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:59.479604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:04.399541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:10.063829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:15.796334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:06.589567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:11.439589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:17.109404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:23.183424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:28.144506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:33.229220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:37.432141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:41.064350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:46.854265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:53.119196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:59.864384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:04.841997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:10.525750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:15.984329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:06.959198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:11.899289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:17.624870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:23.560793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:28.499832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:33.597111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:37.657815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:41.503896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:47.320746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:53.579309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:00.211654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:05.267650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:11.010378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:16.129206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:07.259784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:12.212027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:18.029016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:23.859385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:28.826921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:33.946633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:37.870986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:41.826272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:47.701623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:53.905416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:00.434175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:05.594680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:11.391138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:16.283816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:07.421719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:12.401663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:18.260409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:24.025434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:29.013697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:34.104381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:38.061589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:42.003696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:47.919076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:54.122654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:00.629175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:05.789478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:11.584235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:16.476423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:07.807985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:12.907867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:18.724859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:24.444320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:29.473153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:34.449485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:38.257826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:42.446683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:48.420396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:54.671497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:01.010980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:06.291267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:12.107476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:16.641407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:08.220181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:13.349122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:19.196993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:24.852509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:29.906639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:34.814246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:38.469604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:42.927615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:48.927322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:55.183437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:01.340362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:06.789388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:12.649083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:16.823714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:08.574878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:13.754143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:19.665627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:25.235321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:30.299742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:35.082427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:38.688249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:43.333058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:49.411424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:55.757279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:01.679608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:07.221270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:13.219011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:16.979323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:08.872038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:14.059154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:20.097140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:25.544686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:30.650163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:35.317733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:38.871572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:43.699193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:49.836190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:56.321520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:01.979213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:07.599104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:13.648287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:17.164887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:09.249652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:14.473356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:20.595043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:25.959299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:31.114462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:35.690096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:39.064676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:44.153797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:50.399439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:57.016145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:02.289278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:08.004785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:14.254078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:17.331576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:09.639701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:14.955498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:21.115561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:26.389673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:31.594373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:36.079849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:39.249948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:44.629241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:50.949214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:57.646009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:02.640209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:08.472225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:14.782795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:17.502198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:09.789446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:15.114621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:21.345298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:26.563385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:31.777688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:36.469337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:39.388372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:44.806246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:51.189243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:53:57.978037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:02.799513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:08.649413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-18T12:54:14.977775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-01-18T12:54:33.962184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AMT_ANNUITYAMT_CREDIT_MAX_OVERDUEAMT_CREDIT_SUMAMT_CREDIT_SUM_DEBTAMT_CREDIT_SUM_LIMITAMT_CREDIT_SUM_OVERDUECNT_CREDIT_PROLONGCREDIT_ACTIVECREDIT_CURRENCYCREDIT_DAY_OVERDUECREDIT_TYPEDAYS_CREDITDAYS_CREDIT_ENDDATEDAYS_CREDIT_UPDATEDAYS_ENDDATE_FACTSK_ID_BUREAUSK_ID_CURR
AMT_ANNUITY1.0000.0560.2550.3790.1200.0180.0170.0000.0460.0200.0000.1870.2690.2840.004-0.012-0.002
AMT_CREDIT_MAX_OVERDUE0.0561.0000.114-0.0040.0120.0660.0630.0060.0550.0660.023-0.204-0.070-0.047-0.114-0.010-0.002
AMT_CREDIT_SUM0.2550.1141.0000.446-0.0010.010-0.0190.0000.0260.0100.0060.1260.4010.3130.1810.0030.001
AMT_CREDIT_SUM_DEBT0.379-0.0040.4461.0000.0900.0500.0210.0130.0380.0490.0710.4610.6090.6450.0310.008-0.001
AMT_CREDIT_SUM_LIMIT0.1200.012-0.0010.0901.000-0.0040.1450.0230.079-0.0050.0230.0900.1720.1880.016-0.003-0.000
AMT_CREDIT_SUM_OVERDUE0.0180.0660.0100.050-0.0041.0000.0020.0280.0000.9340.0050.0160.0260.038-0.006-0.0020.001
CNT_CREDIT_PROLONG0.0170.063-0.0190.0210.1450.0021.0000.0220.0000.0020.035-0.0290.0680.0260.008-0.001-0.000
CREDIT_ACTIVE0.0000.0060.0000.0130.0230.0280.0221.0000.0080.0800.2390.2900.1210.0020.0000.0030.001
CREDIT_CURRENCY0.0460.0550.0260.0380.0790.0000.0000.0081.0000.0000.0420.0250.0060.0000.0000.0010.001
CREDIT_DAY_OVERDUE0.0200.0660.0100.049-0.0050.9340.0020.0800.0001.0000.0020.0120.0220.035-0.007-0.0020.001
CREDIT_TYPE0.0000.0230.0060.0710.0230.0050.0350.2390.0420.0021.0000.0710.3140.0130.0000.0070.002
DAYS_CREDIT0.187-0.2040.1260.4610.0900.016-0.0290.2900.0250.0120.0711.0000.7420.7440.8740.0110.000
DAYS_CREDIT_ENDDATE0.269-0.0700.4010.6090.1720.0260.0680.1210.0060.0220.3140.7421.0000.8100.8810.0120.001
DAYS_CREDIT_UPDATE0.284-0.0470.3130.6450.1880.0380.0260.0020.0000.0350.0130.7440.8101.0000.8720.0190.000
DAYS_ENDDATE_FACT0.004-0.1140.1810.0310.016-0.0060.0080.0000.000-0.0070.0000.8740.8810.8721.0000.016-0.001
SK_ID_BUREAU-0.012-0.0100.0030.008-0.003-0.002-0.0010.0030.001-0.0020.0070.0110.0120.0190.0161.0000.000
SK_ID_CURR-0.002-0.0020.001-0.001-0.0000.001-0.0000.0010.0010.0010.0020.0000.0010.000-0.0010.0001.000

Missing values

2025-01-18T12:54:17.864402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-18T12:54:19.868061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-18T12:54:25.714830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SK_ID_CURRSK_ID_BUREAUCREDIT_ACTIVECREDIT_CURRENCYDAYS_CREDITCREDIT_DAY_OVERDUEDAYS_CREDIT_ENDDATEDAYS_ENDDATE_FACTAMT_CREDIT_MAX_OVERDUECNT_CREDIT_PROLONGAMT_CREDIT_SUMAMT_CREDIT_SUM_DEBTAMT_CREDIT_SUM_LIMITAMT_CREDIT_SUM_OVERDUECREDIT_TYPEDAYS_CREDIT_UPDATEAMT_ANNUITY
02153545714462Closedcurrency 1-4970-153.0-153.0NaN091323.000.00NaN0.0Consumer credit-131NaN
12153545714463Activecurrency 1-20801075.0NaNNaN0225000.00171342.00NaN0.0Credit card-20NaN
22153545714464Activecurrency 1-2030528.0NaNNaN0464323.50NaNNaN0.0Consumer credit-16NaN
32153545714465Activecurrency 1-2030NaNNaNNaN090000.00NaNNaN0.0Credit card-16NaN
42153545714466Activecurrency 1-62901197.0NaN77674.502700000.00NaNNaN0.0Consumer credit-21NaN
52153545714467Activecurrency 1-273027460.0NaN0.00180000.0071017.38108982.620.0Credit card-31NaN
62153545714468Activecurrency 1-43079.0NaN0.0042103.8042103.800.000.0Consumer credit-22NaN
71622975714469Closedcurrency 1-18960-1684.0-1710.014985.0076878.450.000.000.0Consumer credit-1710NaN
81622975714470Closedcurrency 1-11460-811.0-840.00.00103007.700.000.000.0Consumer credit-840NaN
91622975714471Activecurrency 1-11460-484.0NaN0.004500.000.000.000.0Credit card-690NaN
SK_ID_CURRSK_ID_BUREAUCREDIT_ACTIVECREDIT_CURRENCYDAYS_CREDITCREDIT_DAY_OVERDUEDAYS_CREDIT_ENDDATEDAYS_ENDDATE_FACTAMT_CREDIT_MAX_OVERDUECNT_CREDIT_PROLONGAMT_CREDIT_SUMAMT_CREDIT_SUM_DEBTAMT_CREDIT_SUM_LIMITAMT_CREDIT_SUM_OVERDUECREDIT_TYPEDAYS_CREDIT_UPDATEAMT_ANNUITY
17164184330075057708Closedcurrency 1-13890-1299.0-1299.00.00334158.4350.00.00.0Consumer credit-1299NaN
17164193527905057718Closedcurrency 1-18080-1596.0-1625.08100.0028248.8400.00.00.0Consumer credit-1625NaN
17164203527905057725Closedcurrency 1-990-83.0-98.0NaN027000.0000.00.00.0Consumer credit-18NaN
17164213757555057734Closedcurrency 1-13350-1152.0-1152.0NaN0195408.0000.0NaN0.0Consumer credit-1139NaN
17164223757555057742Closedcurrency 1-2648031129.0-189.0NaN0202500.0000.0NaN0.0Credit card-109NaN
17164232593555057750Activecurrency 1-440-30.0NaN0.0011250.00011250.00.00.0Microloan-19NaN
17164241000445057754Closedcurrency 1-26480-2433.0-2493.05476.5038130.8400.00.00.0Consumer credit-2493NaN
17164251000445057762Closedcurrency 1-18090-1628.0-970.0NaN015570.000NaNNaN0.0Consumer credit-967NaN
17164262468295057770Closedcurrency 1-18780-1513.0-1513.0NaN036000.0000.00.00.0Consumer credit-1508NaN
17164272468295057778Closedcurrency 1-4630NaN-387.0NaN022500.0000.0NaN0.0Microloan-387NaN